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In mathematics, the log sum inequality is an inequality which is useful for proving several theorems in information theory.

Statement

Let $a_{1},\ldots ,a_{n}$ and $b_{1},\ldots ,b_{n}$ be nonnegative numbers. Denote the sum of all $a_{i}\;$s by $a$ and the sum of all $b_{i}\;$s by $b$. The log sum inequality states that

$\sum _{i=1}^{n}a_{i}\log {\frac {a_{i}}{b_{i}}}\geq a\log {\frac {a}{b}},$

with equality if and only if ${\frac {a_{i}}{b_{i}}}$ are equal for all $i$.

Proof

Notice that after setting $f(x)=x\log x$ we have

${\begin{aligned}\sum _{i=1}^{n}a_{i}\log {\frac {a_{i}}{b_{i}}}&{}=\sum _{i=1}^{n}b_{i}f\left({\frac {a_{i}}{b_{i}}}\right)=b\sum _{i=1}^{n}{\frac {b_{i}}{b}}f\left({\frac {a_{i}}{b_{i}}}\right)\\&{}\geq bf\left(\sum _{i=1}^{n}{\frac {b_{i}}{b}}{\frac {a_{i}}{b_{i}}}\right)=bf\left({\frac {1}{b}}\sum _{i=1}^{n}a_{i}\right)=bf\left({\frac {a}{b}}\right)\\&{}=a\log {\frac {a}{b}},\end{aligned}}$

where the inequality follows from Jensen's inequality since ${\frac {b_{i}}{b}}\geq 0$, $\sum _{i}{\frac {b_{i}}{b}}=1$, and $f$ is convex.

Applications

The log sum inequality can be used to prove several inequalities in information theory such as Gibbs' inequality or the convexity of Kullback-Leibler divergence.

For example to prove Gibbs' inequality it is enough to substitute $p_{i}\;$s for $a_{i}\;$s, and $q_{i}\;$s for $b_{i}\;$s to get

$D_{\mathrm {KL} }(P\|Q)\equiv \sum _{i=1}^{n}p_{i}\log _{2}{\frac {p_{i}}{q_{i}}}\geq 1\log {\frac {1}{1}}=0.$

Generalizations

The inequality remains valid for $n=\infty$ provided that $a<\infty$ and $b<\infty$. Generalizations to convex functions other than the logarithm is given in Csiszár, 2004.

References

• T.S. Han, K. Kobayashi, Mathematics of information and coding. American Mathematical Society, 2001. ISBN 0-8218-0534-7.
• Information Theory course materials, Utah State University [1]. Retrieved on 2009-06-14.
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